Forwarded from Data Analytics
These 9 lectures from Stanford are a pure goldmine for anyone wanting to learn and understand LLMs in depth
Lecture 1 - Transformer: https://lnkd.in/dGnQW39t
Lecture 2 - Transformer-Based Models & Tricks: https://lnkd.in/dT_VEpVH
Lecture 3 - Tranformers & Large Language Models: https://lnkd.in/dwjjpjaP
Lecture 4 - LLM Training: https://lnkd.in/dSi_xCEN
Lecture 5 - LLM tuning: https://lnkd.in/dUK5djpB
Lecture 6 - LLM Reasoning: https://lnkd.in/dAGQTNAM
Lecture 7 - Agentic LLMs: https://lnkd.in/dWD4j7vm
Lecture 8 - LLM Evaluation: https://lnkd.in/ddxE5zvb
Lecture 9 - Recap & Current Trends: https://lnkd.in/dGsTd8jN
Start understanding #LLMs in depth from the experts. Go through each step-by-step video.
https://news.1rj.ru/str/DataAnalyticsX🔗
Lecture 1 - Transformer: https://lnkd.in/dGnQW39t
Lecture 2 - Transformer-Based Models & Tricks: https://lnkd.in/dT_VEpVH
Lecture 3 - Tranformers & Large Language Models: https://lnkd.in/dwjjpjaP
Lecture 4 - LLM Training: https://lnkd.in/dSi_xCEN
Lecture 5 - LLM tuning: https://lnkd.in/dUK5djpB
Lecture 6 - LLM Reasoning: https://lnkd.in/dAGQTNAM
Lecture 7 - Agentic LLMs: https://lnkd.in/dWD4j7vm
Lecture 8 - LLM Evaluation: https://lnkd.in/ddxE5zvb
Lecture 9 - Recap & Current Trends: https://lnkd.in/dGsTd8jN
Start understanding #LLMs in depth from the experts. Go through each step-by-step video.
https://news.1rj.ru/str/DataAnalyticsX
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Design patterns are proven solutions to common problems in development. If you've ever found yourself constantly writing the same thing when creating objects or struggling with managing different types of objects, then the factory pattern might be exactly what you need.
In this tutorial:
https://www.freecodecamp.org/news/how-to-use-the-factory-pattern-in-python-a-practical-guide/
you'll learn what a factory is, why it's useful, and how to implement it in #Python. We'll gather practical examples that will show when and how to apply this pattern in real tasks.
The code can be found on #GitHub
https://github.com/balapriyac/python-basics/tree/main/design-patterns/factory
https://news.1rj.ru/str/CodeProgrammer
In this tutorial:
https://www.freecodecamp.org/news/how-to-use-the-factory-pattern-in-python-a-practical-guide/
you'll learn what a factory is, why it's useful, and how to implement it in #Python. We'll gather practical examples that will show when and how to apply this pattern in real tasks.
The code can be found on #GitHub
https://github.com/balapriyac/python-basics/tree/main/design-patterns/factory
https://news.1rj.ru/str/CodeProgrammer
❤7👍1
Forwarded from Machine Learning
📌 Your First 90 Days as a Data Scientist
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-14 | ⏱️ Read time: 8 min read
A practical onboarding checklist for building trust, business fluency, and data intuition
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-14 | ⏱️ Read time: 8 min read
A practical onboarding checklist for building trust, business fluency, and data intuition
#DataScience #AI #Python
❤3
A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/
tags: #python #deeplearning
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We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations.
In addition, you'll find many other useful materials on machine learning on the site.
Find and use it — https://mlu-explain.github.io/neural-networks/
tags: #AI #ML #PYTHON
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Forwarded from Learn Python Hub
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Data scientists are in high demand right now: there's just too much data to analyze.
In this course, Tatev and Vae teach #Python for #DataScience.
You'll be doing projects and exploring EDA, A/B testing, BI, and more.
https://news.1rj.ru/str/Python53🌟
In this course, Tatev and Vae teach #Python for #DataScience.
You'll be doing projects and exploring EDA, A/B testing, BI, and more.
https://news.1rj.ru/str/Python53
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Data Science Roadmap.pdf
15.5 MB
🏷 Comprehensive Data Science Roadmap Notes
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
🌐 #DataScience #DataScience #pytorch #python #Roadmap
https://news.1rj.ru/str/CodeProgrammer
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
🌐 #DataScience #DataScience #pytorch #python #Roadmap
https://news.1rj.ru/str/CodeProgrammer
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🎯 2026 IT Certification Prep Kit – Free!
🔥Whether you're preparing for #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!
✅ What’s Inside:
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・IT Certs E-book: https://bit.ly/3Mlu5ez
・IT Exams Skill Test: https://bit.ly/3NVrgRU
・Free Cloud Study Guide: https://bit.ly/4kgFVDs
・Free AI material and support tools：https://bit.ly/46qvpDX
👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB
💬 Want exam help? Chat with an admin now!
wa.link/8fy3x4
🔥Whether you're preparing for #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!
✅ What’s Inside:
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/3M9h5su
・IT Certs E-book: https://bit.ly/3Mlu5ez
・IT Exams Skill Test: https://bit.ly/3NVrgRU
・Free Cloud Study Guide: https://bit.ly/4kgFVDs
・Free AI material and support tools：https://bit.ly/46qvpDX
👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB
💬 Want exam help? Chat with an admin now!
wa.link/8fy3x4
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1. Makes the first letter capitalized
.capitalize()
2. Lowers or raises the case of a string
.lower()
.upper()3. Centers the string with symbols around it: 'Python' → 'Python'
.center(10, '*')
4. Counts the occurrences of a specific character
.count('0')5. Finds the positions of specified characters
.find()
.index()
6. Searches for a desired object and replaces it
.replace()
7. Splits the string, removing the split point from it
.split()8. Checks what the string consists of
.isalnum()
.isnumeric()
.islower()
.isupper()tags: #useful
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Forwarded from PyData Careers
Stop using
In Python, when you write:
you're not directly checking if
Yes, in many cases, the result will be the same as for the code:
But the behavior of these two variants is different, and this difference is important.
When you use:
Python calls the
If
Example:
Here, it can be seen that
Therefore, when using
On the other hand, when you write:
you're using the
The
❤️ Therefore, it is always recommended, and this is best practice, to use
👉 https://news.1rj.ru/str/DataScienceQ
if obj == None, use if obj is NoneIn Python, when you write:
obj == None
you're not directly checking if
obj is the value None. Instead, you're asking if the object is equal to None.Yes, in many cases, the result will be the same as for the code:
obj is None
But the behavior of these two variants is different, and this difference is important.
When you use:
obj == None
Python calls the
__eq__ method on the object. That is, the object itself decides what it means to be "equal to None". And this method can be overridden.If
obj is an instance of a class in which __eq__ is implemented so that when compared with None, it returns True (even if the object is not actually None), then obj == None may mistakenly give True.Example:
class Weird:
def __eq__(self, other):
return True # Always asserts that it's equal
obj = Weird()
print(obj == None) # True
print(obj is None) # False
Here, it can be seen that
obj == None returns True due to the custom behaeqf the __eq__ operator in the class.Therefore, when using
obj == None, the result is not always predictable.On the other hand, when you write:
obj is None
you're using the
is operator, which cannot be overridden. This means that the result will always be the same and predictable.The
is operator checks the identity of objects, that is, whether two references point to the same object. Since None is a singleton (the only instance), obj is None is the correct and most efficient way to perform such a check.obj is None instead of obj == None for predictability and efficiency.Please open Telegram to view this post
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Forwarded from .
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